solution.py

@@ -1,35 +0,0 @@
-import numpy as np
-from sklearn import preprocessing
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.pipeline import make_pipeline
-from sklearn.feature_extraction.text import TfidfVectorizer
-import lzma
-
-le=preprocessing.LabelEncoder()
-
-def open_file_xz(file: str):
-    with lzma.open(file, mode='rt') as f:
-        return f.readlines()
-
-def open_file(file: str):
-    with open(file) as f:
-        return f.readlines()
-
-def train_model(train_in, train_exp):
-    train_exp = le.fit_transform(train_exp)
-    pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
-    model=pipeline.fit(train_in, train_exp)
-    return model
-
-def make_prediction(catalog: str):
-    train_in = open_file_xz("train/in.tsv.xz")
-    train_exp = open_file("train/expected.tsv")
-    to_be_predict = open_file_xz(catalog + "/in.tsv.xz")
-
-    model = train_model(train_in,train_exp)
-    
-    prediction=model.predict(to_be_predict)
-    np.savetxt(catalog + "/out.tsv", prediction, fmt='%d', delimiter='\n')
-
-make_prediction("dev-0")
-make_prediction("test-A")